Clinical decision support system for patient-ventilator asynchrony detection and management

ABSTRACT

The present disclosure describes a system that automatically detects patient-ventilator asynchrony and trends in patient-ventilator asynchrony. The present disclosure describes a framework that uses pressure, flow, and volume waveforms to detect patient-ventilator asynchrony and the presence of secretions in the ventilator circuit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation application of U.S. application Ser.No. 16/762,224, filed May 7, 2020, which application is a National Stageapplication under 35 U.S.C. § 371 of International Application No.PCT/US18/60056, filed Nov. 9, 2018, which application relies on thedisclosure of and claims priority to and the benefit of the filing dateof U.S. Provisional Application No. 62/583,558, filed Nov. 9, 2017, thedisclosure of which is incorporated by reference herein in its entirety.

GOVERNMENT RIGHTS

This invention was made with the support of the United States Governmentunder Grant number IIP-1456404 by the National Science Foundation. TheUnited States Government has certain rights in the invention.

BACKGROUND

Patient-ventilator asynchrony, also referred to as dyssynchrony, refersto a mismatch between ventilator delivery and patient demand. To addresspatient-ventilator asynchrony, clinicians can adjust ventilator settingsor ventilation modes, change a patient's sedation level, or performother interventions. However, continuous monitoring of mechanicallyventilated patients by clinical staff is infeasible.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

SUMMARY

The present invention provides methods and systems for detectingpatient-ventilator asynchrony. One method describes how to calculate awaveform from mechanical ventilator pressure and flow waveforms thatindicates patient-ventilator interactions whereby a plurality of peaksand valleys of the resulting waveform indicates asynchronouspatient-ventilator interactions. Then a method is described that usessaid calculated patient-ventilator interaction indicator waveform todetermine various types of patient-ventilator asynchrony types. Thismethod comprises using a collection of ventilator pressure and flowwaveforms from prior patients and/or through generating a plurality ofsynthetic ventilator pressure and flow waveforms, calculating saidinteraction indicator waveform for each individual breath cycle of thesynthetic waveforms, extracting features from the interaction indicatorwaveform, and training a machine learning algorithm to perform mappingbetween features and patient-ventilator asynchrony types. Training datatypically involves assigning labels associated with different asynchronytypes to waveforms from each breath. In the case of using prior patientdata for training, such labels can be generated by clinical expertsreviewing the waveforms or by clinical experts reviewing waveforms aswell as other appropriate clinical signals such as esophageal pressureor diaphragm activity. In the case of using synthetic data, actualasynchrony type is known a priori when generating such syntheticwaveforms. A system is also described that acquires ventilator pressureand flow waveform data from a data source, generates said interactionindicator waveform from the waveform data received, extracts a set offeatures from said indicator waveform, and classifies each individualbreath cycle into categories of patient-ventilator asynchrony. Thesystems and methods can also be configured to include recommendation,alert, and/or notification modules or steps to notify end user(s) suchas clinicians in a manner sufficient to address the asynchrony and/oradjust ventilator settings to avoid asynchrony, assess a patientexperiencing asynchrony and make an appropriate action, alert hospitalstaff to, and/or change internal settings automatically, optimize orimprove ventilator performance (e.g., triggering and cycling timings),or identify other relevant parameters that affect ventilator function,and/or to allow for intervention including changes to a patient'ssedation level. The systems and methods can be implemented on aperipheral device communicating with a ventilator or embedded in aventilator.

An embodiment described of the system further includes a graphical userinterface (GUI) for communicating detected patient-ventilator asynchronywhereby the system can be used as a clinical decision support system. Insome embodiments, the GUI can display educational information on thedetected asynchronies to further assist in managing mechanicallyventilated patients.

According to a first aspect of the invention, there is provided systemfor detecting patient ventilator interaction comprising:

a detection module comprising computer executable instructions inoperable communication with one or more computer processor, thedetection module configured for and in operable communication with aventilator for acquiring mechanical ventilator airway pressure, flow,and/or volume waveform data; and

a patient ventilator interaction indicator module in operablecommunication with the detection module, the patient ventilatorinteraction indicator module comprising computer executable instructionsin operable communication with one or more computer processor for:

-   -   generating a patient ventilator interaction indicator waveform        comprising a Delta waveform from said ventilator waveform data,        wherein the Delta waveform represents the difference between        normalized pressure, after correcting for positive end        expiratory pressure (PEEP), and normalized flow waveforms;    -   extracting a set of features from said patient ventilator        interaction indicator waveform; and    -   determining and indicating a type of patient ventilator        asynchrony present, or absence thereof, associated with one or        more breath cycle, which patient ventilator asynchrony or        absence thereof is based on the extracted set of features.

BRIEF DESCRIPTION OF THE DRAWINGS

It should be noted in the following description that like or the samereference numerals in different embodiments denote the same or similarfeatures.

Notwithstanding any other forms which may fall within the scope of thepresent invention, a preferred embodiment/preferred embodiments of theinvention will now be described, by way of example only, with referenceto the accompanying drawings in which:

FIG. 1 is a schematic that illustrates the overall architecture of anembodiment of the clinical decision support system;

FIG. 2 is a flowchart that illustrates an embodiment of the componentsof the asynchrony detection algorithm;

FIG. 3 is a waveform that depicts the flow waveform of breath showingevidence of secretions (‘Secretions’) compared to the flow waveform of anormal breath without evidence of secretions (‘No secretions’);

FIG. 4 is a graph that depicts the absolute value of the Hilberttransform of a segment of the expiratory phase of two breaths: breathwith evidence of secretions (‘Secretions’) and breath without secretions(‘No secretions’);

FIG. 5 is a schematic that illustrates an electrical analogue of therespiratory system;

FIG. 6 is a graph that depicts an example of a patient-ventilatorinteraction indicating waveform (“Delta”) of a sample breath cycle ofpremature termination asynchrony type;

FIG. 7 is a graph that depicts an example of a patient-ventilatorinteraction indicating waveform (“Delta”) of a sample breath cycle ofdelayed termination asynchrony type;

FIGS. 8A to 8C are waveforms that respectively depict an example ofpressure (FIG. 8A), flow (FIG. 8B), Delta (FIG. 8C) waveforms for anineffective triggering asynchrony type;

FIGS. 9A and 9B are waveforms that respectively depict an example of asynthetic set of pressure (FIG. 9A) and flow (FIG. 9B) waveforms for apremature termination asynchrony type in volume control ventilationmode;

FIGS. 10A and 10B are waveforms that respectively depict an example of asynthetic set of pressure (FIG. 10A) and flow (FIG. 10B) waveforms for adelayed termination asynchrony type in volume control ventilation mode;

FIGS. 11A and 11B are waveforms that respectively depict an example of asynthetic set of pressure (FIG. 11A) and flow (FIG. 11B) waveforms for apassive patient with no spontaneous muscle activity in volume controlventilation mode;

FIGS. 12A and 12B are waveforms that respectively depict an example of asynthetic set of pressure (FIG. 12A) and flow (FIG. 12B) waveforms forpatient effort in sync with ventilator cycling in pressure controlventilation mode;

FIGS. 13A to 13C are waveforms that respectively depict an example setof pressure (FIG. 13A), flow (FIG. 13B) and Delta waveforms (FIG. 13C)for a premature termination asynchrony type in pressure controlventilation mode;

FIGS. 14A to 14C are waveforms that respectively depict an example setof pressure (FIG. 14A), flow (FIG. 14B) and Delta waveforms (FIG. 14C)for a premature termination asynchrony type in volume controlventilation mode;

FIGS. 15A to 15C are waveforms that respectively depict an example setof pressure (FIG. 15A), flow (FIG. 15B) and Delta waveforms (FIG. 15C)for a delayed termination asynchrony type in pressure controlventilation mode;

FIGS. 16A to 16C are waveforms that respectively depict an example setof pressure (FIG. 16A), flow (FIG. 16B) and Delta waveforms (FIG. 16C)for a delayed termination asynchrony type in volume control ventilationmode;

FIGS. 17A to 17C are waveforms that respectively depict an example setof pressure (FIG. 17A), flow (FIG. 17B) and Delta waveforms (FIG. 17C)for a passive patient in volume control ventilation mode;

FIGS. 18A to 18C are waveforms that respectively depict an example setof pressure (FIG. 18A), flow (FIG. 18B) and Delta waveforms (FIG. 18C)for a passive patient in pressure control ventilation mode;

FIGS. 19A to 19C are waveforms that respectively depict an example setof pressure (FIG. 19A), flow (FIG. 19B) and Delta waveforms (FIG. 19C)for patient effort in sync with ventilator cycling in volume controlventilation mode;

FIGS. 20A to 20C are waveforms that respectively depict an example setof pressure (FIG. 20A), flow (FIG. 20B) and Delta waveforms (FIG. 20C)for patient effort in sync with ventilator cycling in pressure controlventilation mode;

FIGS. 21A to 21C are waveforms that depict an example set of pressure(FIG. 21A), flow (FIG. 21B) and Delta waveforms (FIG. 21C) for patienteffort in sync with ventilator cycling in pressure control ventilationmode;

FIGS. 22A to 22C are waveforms that depict an example set of pressure(FIG. 22A), flow (FIG. 22B) and Delta waveform (FIG. 22C) for patienteffort in sync with ventilator cycling in volume control ventilationmode; and

FIG. 23 is an illustration of an embodiment of a graphical userinterface containing a window with elements communicating detectedpatient-ventilator asynchrony and recommendations for mitigatingasynchrony.

DETAILED DESCRIPTION

Respiratory failure can occur as a result of numerous factors, andpatients in intensive care units (ICUs) are frequently placed onmechanical ventilators. Mechanical ventilation helps maintain lungfunction. Patient-ventilator asynchrony, also referred to asdyssynchrony, refers to a mismatch between ventilator delivery andpatient demand. Patient-ventilator asynchrony can cause discomfort, andis associated with longer lengths of stay in ICUs. To addresspatient-ventilator asynchrony, clinicians can adjust ventilator settingsor ventilation modes, change a patient's sedation level, or performother interventions. Asynchrony can be detected by continuous monitoringof patients and using waveforms generated by mechanical ventilators.However, continuous monitoring of mechanically ventilated patients byclinical staff is infeasible.

The detection of asynchrony and the type of asynchrony requires specifictraining and knowledge of waveform analysis. If patient-ventilatorasynchrony can be reliably detected via an automated algorithm, then thepatient-ventilator asynchrony information can be displayed to the enduser or used within a clinical decision support system for respirationmanagement or within a mechanical ventilator to improve the functioningof the mechanical ventilator (e.g., optimizing triggering and cyclingtiming). The present disclosure describes a system that can be used todetect asynchronies and trends in asynchrony automatically. Theinformation obtained using methods of the present disclosure can be usedto alert hospital staff to adjust ventilator settings to prevent severeasynchrony. Algorithms embedded in mechanical ventilators can usepatient-ventilator asynchrony information to change internal settingsautomatically, optimize or improve ventilator performance (e.g.,triggering and cycling timings), or identify other relevant parametersthat affect ventilator function. The system monitoring the patient fordetecting asynchrony can alert a clinician or send a notification to aclinician for intervention if the patient is experiencing severeasynchrony.

Any algorithm described herein can be embodied in software or set ofcomputer-executable instructions capable of being run on a computingdevice or devices. The computing device or devices can include one ormore processor (CPU) and a computer memory. The computer memory can beor include a non-transitory computer storage media such as RAM whichstores the set of computer-executable (also known herein as computerreadable) instructions (software) for instructing the processor(s) tocarry out any of the algorithms, methods, or routines described in thisdisclosure. As used in the context of this disclosure, a non-transitorycomputer-readable medium (or media) can include any kind of computermemory, including magnetic storage media, optical storage media,nonvolatile memory storage media, and volatile memory. Non-limitingexamples of non-transitory computer-readable storage media includefloppy disks, magnetic tape, conventional hard disks, CD-ROM, DVD-ROM,BLU-RAY, Flash ROM, memory cards, optical drives, solid state drives,flash drives, erasable programmable read only memory (EPROM),electrically erasable programmable read-only memory (EEPROM),non-volatile ROM, and RAM. The computer-readable instructions can beprogrammed in any suitable programming language, including JavaScript,C, C #, C++, Java, Python, Perl, Ruby, Swift, Visual Basic, andObjective C. Embodiments of the invention also include a non-transitorycomputer readable storage medium having any of the computer-executableinstructions described herein.

A skilled artisan will further appreciate, in light of this disclosure,how the invention can be implemented, in addition to software andhardware, using one or more firmware. As such, embodiments of theinvention can be implemented in a system which includes any combinationof software, hardware, or firmware. In the context of thisspecification, the term “firmware” can include any software programmedonto the computing device, such as a device's nonvolatile memory. Thus,systems of the invention can also include, alternatively or in additionto the computer-executable instructions, various firmware modulesconfigured to perform the algorithms of the invention.

According to embodiments, the computing device or devices can include amainframe computer, web server, database server, desktop computer,laptop, tablet, netbook, notebook, personal digital assistant (PDA),gaming console, e-reader, smartphone, or smartwatch, which may includefeatures such as a processor, memory, hard drive, graphics processingunit (GPU), and input/output devices such as display, keyboard, andmouse or trackpad (depending on the device). Embodiments can alsoprovide a graphical user interface made available on one or more clientcomputers. The graphical user interface can allow a user on a clientcomputer remote access to the method or algorithm.

Additional embodiments of the invention can include a networked computersystem for carrying out one or more methods of the invention. Thecomputer system can include one or more computing devices which caninclude a processor for executing computer-executable instructions, oneor more databases, a user interface, and a set of instructions (e.g.,software) for carrying out one or more methods of the invention.According to other embodiments, the computing device or devices can beconnected to a network through any suitable network protocol such as IP,TCP/IP, UDP, or ICMP, such as in a client-server configuration and oneor more database servers. The network can use any suitable networkprotocol and can be any suitable wired or wireless network including anylocal area network, wide area network, Internet network,telecommunications network, Wi-Fi enabled network, or Bluetooth enablednetwork.

Patient-Ventilator Asynchrony Types

The primary types (categories) of patient-ventilator asynchrony includedouble triggering, ineffective triggering, premature termination,delayed termination, and flow starvation (i.e., excessive respiratorymuscle activity during inspiration for example due to inadequate flow orvolume). Another asynchrony-related event includes evidence of airtrapping. Evidence of secretions (i.e., fluid build-up in the lungs andventilator circuit) or fluid in the ventilator circuit can be detectedusing waveforms. The disclosed clinical decision support system cannotify clinicians to consider performing suction on a patient whensecretions are detected. When neither premature termination nor delayedtermination are used to classify a breath cycle, no cycling asynchronycan be used as the patient-ventilator asynchrony type. Furthermore, ifno asynchrony is detected in a breath cycle, the breath's asynchronytype is “no asynchrony”.

Delayed Termination—Delayed termination occurs when the end ofmechanical inspiration exceeds a patient's neural inspiration—theexpiratory valve opens after the patient has already initiatedexhalation. A consequence of delayed termination is air trapping andineffective triggering as a result of insufficient expiratory time andexcessive tidal volume.

Premature Termination—Premature termination occurs when the end ofmechanical inspiration precedes the end of a patient's neuralinspiration—the expiratory valve prematurely opens before the patientstops inhaling. Premature termination subjects a patient to an increasedrisk of double triggering. When shortened expiration results in a doubletrigger, air trapping and auto-PEEP can occur, and can inhibit a patientfrom reaching subsequent trigger thresholds.

Ineffective Triggering—Ineffective triggers occur when patientinspiration occurs during mechanical expiration, when a patient istrying to inspire and trigger another breath towards the end of themechanical expiration phase but the ventilator does not deliver supportfor those patient triggering efforts.

Inadequate Support—In certain scenarios including inadequate delivery offlow or volume (also referred to as flow starvation or air hunger involume control ventilation), i.e., when a patient desires more air thanwhat is delivered by the ventilator during inspiration, a patientexcessively engages the respiratory muscles during inspiration.Similarly, excessive respiratory muscle activity during inspiration canoccur due to inadequate pressure support in the pressure controlventilation or pressure support ventilation modes. Finally, the patientmay attempt to initiate a new breath by engaging the respiratory musclesduring inspiration, which is referred to as ineffective effort duringinspiration.

Double Triggering—Double triggering is the delivery of a second breathwhen the previous breath has not fully completed its inspiratory(inhalation) and expiratory (exhalation) phases. Double triggeringinvolves a patient receiving a breath with an expiratory time less thanone half of the mean inspiratory time or when the duration of expirationis less than a defined threshold (e.g., 500 msec). Double triggering mayresult in accumulation of air in the lungs (air trapping) such that thevolume of air in the lungs at the end of the breath is greater than thatat the start of the breath. Double triggering is a very seriousasynchrony and can lead to barotrauma, where the lungs becomehyperinflated.

Air Trapping—Air trapping occurs when a volume of inspired air isexpired incompletely.

Aborted Breath—A breath is referred to as an aborted breath if themaximum tidal volume of a breath is less than a defined threshold (e.g.,½ of set tidal volume). An aborted breath can cause a patient to rejecta breath delivered by the ventilator.

Irregular Breath—A breath is referred to as irregular if the shape ofthe waveform is substantially different from the pressure and flowwaveform of a normal breath or a breath with asynchrony types discussedabove. Breaths with substantially different shapes, where a reliableanalysis of the breath is not possible, are considered irregularbreaths.

No Asynchrony—If no asynchrony is detected in a breath cycle, thebreath's asynchrony type is “no asynchrony” or “normal”.

Overall Architecture of the Clinical Decision Support System

The present disclosure describes a system for detectingpatient-ventilator asynchrony. In one embodiment, this clinical decisionsupport system comprises the following components:

-   -   1. Communication Module—The Communication Module continuously        downloads waveform and other data, such as settings and alarms,        from a data source (e.g., mechanical ventilator, patient        monitor) using an available communications port on the data        source. If the patient-ventilator asynchrony detection system is        implemented on a ventilator, the Communication Module        continuously downloads waveform and other data from the        appropriate internal component of the ventilator.    -   2. Data Processing Module—The Data Processing Module analyzes        waveforms and other available data to detect patient-ventilator        asynchrony events and asynchrony types. This comprises the steps        of generating patient-ventilator interaction indicator        waveforms, extracting features from the indicator waveforms, and        classifying each individual breath cycle into categories of        patient-ventilator asynchrony.    -   3. Data Logging Module—The Data Logging Module saves waveform,        other extracted information, and the results of the analysis in        a database.    -   4. Recommendation Module—The Recommendation Module uses detected        asynchrony events, asynchrony types, and other detected events        (e.g., detection of secretions or fluid in the circuit, or        evidence of air trapping) to provide recommendations to an end        user.    -   5. User Interface—The User Interface provides visualization of        detected events and trends, and provides recommendations from        the Recommendation Module to the end user. The end user can also        review past waveforms to verify detected events by the system.    -   6. Transmission (Optional)—A transmission module is responsible        for transmitting detected events and asynchrony information to a        remote location (e.g., a remote server, a router, or a        smartphone or tablet). A notification can alert clinicians to        assess a patient experiencing asynchrony and make an appropriate        action.

FIG. 1 illustrates the overall architecture of exemplary embodiments ofthe clinical decision support system.

Detection of Patient-Ventilator Asynchrony and Secretions UsingWaveforms

The present disclosure describes a framework that uses pressure, flow,and volume waveforms to detect patient-ventilator asynchrony and thepresence of secretions or fluid in the ventilator circuit. In someembodiments, the framework described herein is non-invasive. In someembodiments, the framework descried herein uses waveforms available fromany model of mechanical ventilator.

Patient-Ventilator Asynchrony Detection Algorithm

The framework described herein uses pressure and flow waveforms from amechanical ventilator to detect asynchrony and other events, such as thepresence of secretions or air trapping. The first step involvessegmenting a continuous waveform to identify individual breath cycles.Breath cycles are then analyzed further using a series of classifiersand labeling algorithms.

The overall framework described herein comprises a cascade ofclassifiers and labeling algorithms, where a breath cycle is fed into aseries of classifiers and labeling algorithms in sequence. In someembodiments, the classifiers or labeling algorithms are rule-based,i.e., if a certain condition holds, the breath is classified or labeledas a certain asynchrony/respiratory event. In some embodiments, theasynchrony or event under consideration is not present. In someembodiments, the classifiers or labeling algorithms use supervisedlearning classification algorithms, such as random forests or neuralnetworks, where the parameters of the classifier model are identifiedbeforehand through a training process. Classifiers (e.g., randomforests, neural networks, or support vector machines) that belong to theclass of supervised learning classifiers are referred to as statisticalclassifiers or machine learning classifiers. The classifiers arearranged in a sequential framework. In some embodiments, a breath cycleis assigned to an appropriate class if a certain asynchrony is detected.In some embodiments, a breath cycle is assigned to a class where theasynchrony under consideration is absent, and the next classifier in thechain is engaged to analyze the breath cycle.

An example of a framework described herein used to detectpatient-ventilator asynchrony and other related events comprises thefollowing steps:

-   -   1. Acquire waveform data, including at least the flow and        pressure from the ventilator.    -   2. Perform breath cycle segmentation, where the continuous        waveform is analyzed to find individual breath cycles (i.e.,        start of inspiration to end of expiration, or using data        provided by a ventilator to detect the start and end of a        breath).    -   3. Select a breath cycle in the queue of breath cycles to be        analyzed.    -   4. Perform signal denoising and filtering on the waveforms of        the selected breath cycle to remove artifacts.    -   5. If volume waveform is not available, generate the volume        waveform from the flow waveform using cumulative integration        over time.    -   6. If evidence of secretion exists, add the secretion label to        the breath cycle.    -   7. Check whether the breath is considered an aborted breath. An        aborted breath is a breath cycle where the total delivered        volume of the breath is less than a specific threshold (e.g., <½        set tidal volume). If an aborted breath is detected, then        classify the current breath cycle as an aborted breath and        proceed to Step 3.    -   8. Check if the breath cycle satisfies the conditions of double        triggering. If double triggering is detected, then classify the        current breath as a double trigger and proceed to Step 3.    -   9. Check if the breath satisfies the conditions of air trapping.        Air trapping is present if the value of the flow at end        expiration is less than a defined negative number (e.g., −2        L/min) or if the difference between the inspired volume and        expired volume is larger than a defined threshold (e.g., expired        breath volume is less than 90% of inspired volume). If air        trapping is detected, add the air trapping label.    -   10. Generate a newly-derived waveform (i.e., Delta waveform)        from the normalized pressure and flow waveforms.    -   11. Extract features from the Delta waveform.    -   12. Check whether the breath is irregular. An irregular breath        is a breath cycle with a waveform shape that is substantially        different than a typical waveform (including normal and        asynchronous breaths) or a breath with missing features on the        Delta waveform. If an irregular breath is detected, then        classify the current breath as irregular and proceed to Step 3.    -   13. Check for evidence of inadequate support during inspiration.        If inadequate support is detected, add the inadequate support        label.    -   14. Use features detected from the Delta waveform to detect if        ineffective triggering exists. If ineffective triggering is        detected, add an ineffective triggering label.    -   15. Use the extracted features from the Delta waveform as an        input to a statistical (machine learning) classifier, and use        the class assignment provided by the classifier to classify the        breath into premature termination, delayed termination, or no        cycling asynchrony categories.    -   16. If a breath is classified as a premature termination or a        delayed termination, then go to Step 3.    -   17. If no label (e.g., air trapping, ineffective triggering,        etc.) has been assigned to the breath, classify the breath to        the “no-asynchrony” class. Otherwise, classify the breath to the        “some form of asynchrony” class with an appropriate label.    -   18. Go to Step 3.

FIG. 2 illustrates the steps discussed above in the form of a flowchart. In some embodiments, a breath can have a single label. In someembodiments, a breath can have multiple labels (e.g., secretion, airtrapping, or inadequate support), and is assigned to one class.

Segmenting the Waveforms to Breath Cycles

Ventilators can provide the start and end times associated with acomplete breath cycle. If the start and end times of a complete breathcycle are available, such data can be used to divide a continuouswaveform into a series of breath cycles.

If a ventilator does not provide the start and end times associated witha complete breath cycle to segment waveforms into individual breathcycles, different techniques can be used to detect the start of a newbreath. In some embodiments, the start of a new breath can be detectedby identifying when air flow transitions from negative to positive(using an existing air flow sensor embedded in a ventilator) immediatelyprior to a peak positive flow.

Denoising Waveforms

Smoothing and filtering techniques can be used to remove noise andartifacts from pressure, flow, and volume signals. Denoising techniquesused to remove noise and artifacts from pressure, flow, and volumesignals include low pass filtering with finite impulse response (FIR),infinite impulse response (IIR) filters, wavelet denoising, and splinesmoothing.

Generating Volume Waveform

If a volume waveform is unavailable from a mechanical ventilator, thevolume waveform can be generated from the flow waveform for a specificbreath cycle. In some embodiments, the flow waveform can be cumulativelyintegrated from the start to the end of a breath to generate a volumewaveform.

Detecting Evidence of Secretions or Fluid in the Circuit

The presence of airway secretions or fluid in a ventilation circuit canproduce saw tooth patterns on flow-volume loops. The oscillations arepronounced specifically on the flow waveform. FIG. 3 depicts the flowwaveform of breath showing evidence of secretions or fluid in theventilation circuit (‘Secretions’) compared to a normal breath withoutevidence of secretions (‘No secretions’).

Quantifying the amount of high frequency oscillations on flow waveformscan indicate the presence of secretions. The detection method must berobust to avoid false positives caused by other forms of oscillations onthe flow waveform, such as cardiac oscillations. The automated detectionof secretions or fluids in a circuit is important to minimizecomplications resulting from retained secretions and to preventunnecessary suctioning.

To detect oscillations in the flow waveform that could indicatesecretions, the present disclosure isolates high frequency oscillationsin the flow signal, and measures the amplitude of the high frequencyoscillations. In some embodiments, a seventh order IIR zero-phase highpass filter is used to extract oscillations above 5 Hz. To avoidincluding transient signals induced by opening and closing of ventilatorcircuits in the analysis, the middle 80% of expiration phase of thebreath to high pass filter can be extracted and further analyzed.

A Hilbert transform can then be applied to obtain an envelope of thehigh frequency (e.g., >5 Hz) oscillations during exhalation. To assessthe presence of secretions, the root-mean-square (RMS) value of theHilbert transform can be calculated, where RMS of a time series(waveform) is defined as:

$\begin{matrix}{{RMS} = {\sqrt{\frac{1}{n}{\sum_{i = 1}^{n}\; x_{i}^{2}}}.}} & (1)\end{matrix}$

If the RMS value is higher than a particular threshold (e.g., 1-2L/min), a ‘presence of secretion or fluid’ label is assigned to thebreath. FIG. 4 depicts the Hilbert transform of a segment of theexpiratory phase of two breaths: with evidence of secretions(‘Secretions’) and without secretions (‘No secretions’). The presence orabsence of secretions or fluid in the circuit is independent of thepresence of asynchrony. If secretions or fluids are detected, thealgorithm can add a ‘presence of secretion or fluid’ label to the breathcycle. Whether a secretion label is assigned to a breath or not, thebreath cycle is moved to the next step for further analysis.

Detecting Aborted Breaths

In some embodiments, a patient can abort or reject a breath. In someembodiments, aborted breaths are detected by low tidal volumes. In someembodiments, aborted breaths are detected using a rule-based technique:if the maximum delivered volume in the breath cycle is less than aparticular threshold (e.g., one half of set tidal volume or mean tidalvolume), then the breath is classified as an aborted breath and isremoved from further analysis.

Detecting Double Triggering

To detect double triggering, the duration (or absence) of expirationmust be detected. In some embodiments, double triggering is detected byanalyzing the flow waveform of a breath. In some embodiments, the startof expiration of a breath can be determined by detecting the transitionfrom positive to negative air flow. The absence of a transition frompositive to negative air flow indicates an absence of expiration for abreath cycle. In some embodiments, the duration of expiration can becalculated as the duration of time that measured flow has the oppositesign (i.e., negative) as inspired flow.

When the duration of expiration, T_(exp), is identified, then theinspiratory time T_(insp) (i.e., duration of time from start of a breathto start of expiration) can be computed as the difference between breathcycle duration and T_(exp). A rule-based technique can then be used todetect a double triggered breath when certain conditions are satisfied.In some embodiments, the rule used to detect double triggering isT_(exp)<½ mean T_(insp). In some embodiments, the rule used to detectdouble triggering is T_(exp)<½ set T_(insp). In some embodiments, therule used to detect double triggering is T_(exp) less than a definedthreshold (e.g., 0.5 second). If a double triggering event is detected,that breath cycle is assigned to the double trigger class and removedfrom further analysis.

Detecting Air Trapping

In some embodiments, a rule-based technique is used to detect airtrapping. In some embodiments, an air trapping label can be assigned toa breath if the value of flow at the end of expiration is less than adefined threshold (e.g., <−2 L/min). In some embodiments, an airtrapping label is assigned to a breath if the expired volume is lessthan the inspired volume (e.g., expired volume is less than 90% of theinspired volume). If air trapping is detected, then an air trappinglabel is assigned to a breath. Whether an air trapping label is assignedto a breath or not, the breath is moved to the next step and furtheranalyzed for other types of asynchronies.

Generating the Delta Waveform

A critical component of detecting patient-ventilator asynchrony is theextraction of features associated with various types of asynchrony. Thepresent disclosure uses a newly defined waveform referred to as theDelta waveform to extract features associated with cycling asynchrony,ineffective triggering, and inadequate support. The definition of theDelta waveform as a means for indicating patient-ventilator interactionis independent of the mode of ventilation (e.g., pressure controlventilation, volume control ventilation, pressure support ventilation,etc.) and does not require a determination of when to deliverinspiration, expiration, or when to cycle between inspiration andexpiration for its calculation.

The Delta waveform is defined as:

$\begin{matrix}{{{\delta (t)} = {\frac{{p_{aw}(t)} - p_{e}}{{p_{aw}\left( t^{*} \right)} - p_{e}} - \frac{q(t)}{q\left( t^{*} \right)}}},{t \geq t_{0}},} & (2)\end{matrix}$

where t* represents the time of peak flow (i.e., q(t)≤q(t*), t≥t₀), andp_(e) is the positive end expiratory pressure (PEEP). p_(aw) is theairway pressure, q is the air flow, and to is the time at the start ofthe breath cycle. The Delta waveform represents the synchronizedmorphological difference between normalized pressure (after correctingfor PEEP) and normalized flow waveforms. The operation of subtraction ofconstant p_(e) and scaling by the constant (p_(aw)(t*)−p_(e)) to eachdata point in p_(aw) represents the application of normalizing constantsto the ventilator pressure waveform. Likewise, the scaling by thenumerical constant q(t*) to each data point in q represents theapplication of normalizing constants to the ventilator flow waveform.The first term of Equation (2),

$\frac{{p_{aw}(t)} - p_{e}}{{p_{aw}\left( t^{*} \right)} - p_{e}},$

is referred to as embodiment of a normalized ventilator pressurewaveform. The second term of Equation (2),

$\frac{q(t)}{q\left( t^{*} \right)},$

is referred to as an embodiment of a normalized ventilator flowwaveform. In another embodiment of this second term of Equation (2), aconstant q_(e) can also be subtracted from both the numerator anddenominator. In some embodiments, this constant q_(e) can be the valueof flow at end of exhalation (expiration) of the previous breath cycle.In another embodiment, the constant q_(e) can be the initial value ofthe flow waveform. The resultant waveform calculated from Equation (2)is thus an embodiment of taking pairwise relationships betweenventilator pressure and flow waveforms to generate a Delta waveform.

In other embodiments, normalization can be performed with respect to anytime point within the inspiratory phase (mechanical inspiration). Aninspiratory phase is defined as the interval of a breath cycle duringwhich the ventilator is supplying inspiratory support or its inspiratoryvalves are open. An expiratory phase (mechanical expiration) is definedas the interval of a breath cycle during which the ventilator'sexpiratory valves are open thereby allowing the connected patient toexhale. In a first embodiment, normalization is performed with respectto t* (i.e., time of maximum flow) because t* can be robustly detectedon the waveform. In other embodiments, Equation (2) can be modified todefine a new Delta waveform by replacing t* with t**, where t** is anytime point between to and the end of the inspiratory phase of the breathcycle. In a first embodiment, t** can be the time elapsed into a breathcycle that is approximately the pressure rise time as set on theventilator (i.e., time required for the ventilator-controlled pressureprofile to reach a predetermined percentage of the set pressure supportlevel). In a second embodiment, t** can be the time elapsed into abreath cycle that is approximately the flow rise time as set on theventilator (i.e., time required for the ventilator-controlled flow orvolume profile to reach a predetermined percentage of the set peak flowor volume level).

In other embodiments, a suitable choice for t* can be informed byapproximating the dynamics of the respiratory system with a first-orderdifferential equation for airway pressure,

${{p_{aw}(t)} = {{R_{rs}{\overset{.}{V}(t)}} + {\frac{1}{C_{rs}}{V(t)}} - {P_{mus}(t)} + P_{0}}},$

where R_(rs) is resistance of the respiratory system, C_(rs) iscompliance of the respiratory system, p_(mus) is respiratory musclepressure, p₀ is positive end-expiratory pressure (PEEP), V is volume,and {dot over (V)} is air flow. Considering only the first two terms onthe right-hand-side of the equation for p_(aw)(t) representing theresistive and elastic forces, respectively, the resistive forcecontribution is maximized when V is at its maximum value and the elasticforce contribution is minimal when V is at its minimum value. t* can beany suitable time point whereby

${R_{rs}{\overset{.}{V}\left( t^{*} \right)}} > {\frac{1}{C_{rs}}{{V\left( t^{*} \right)}.}}$

Without requiring knowledge of values for R_(rs) and C_(rs), t* can bechosen as a suitable time about the beginning of inspiratory phasewhereby flow is close to its maximum value and volume is close to itsminimum. Any equivalent criteria wherein choosing t* coincides with asuitable time point into mechanical inspiration wherein resistive forcesof the patient-ventilator circuit dominate elastic forces can be used(e.g., multi-compartmental models of the respiratory system) withoutdeparting from the essence of the present disclosure.

The Delta waveform does not inform on when to deliver inspiration,expiration or when to cycle between inspiration and expiration. However,patterns of morphological features extracted from the Delta waveform canindicate when an already delivered breath by a ventilator was out ofsync with patient respiratory efforts (i.e., patient-ventilatorasynchrony event). The following section describes variousDelta-waveform-specific features that can be extracted to classifybreath cycles into categories of patient-ventilator asynchrony.

Feature Extraction from the Delta Waveform

The present disclosure can use a framework to extract a series offeatures from the Delta waveform that is used in the next steps of thealgorithm for classification. The following definitions are used in thedescribed framework.

Normalized pressure is defined as:

$\begin{matrix}{{{{\overset{¯}{p}(t)} = \frac{{p_{aw}(t)} - p_{e}}{{p_{aw}\left( t^{*} \right)} - p_{e}}},{t \geq t_{0}}},} & (8)\end{matrix}$

where t* represents the time of maximum flow (i.e., q(t)≤q(t*), t≥t₀)and p_(e) is the extrinsic positive end expiratory pressure. Normalizedflow is defined as:

$\begin{matrix}{{{{\overset{¯}{q}(t)} = \frac{q(t)}{q\left( t^{*} \right)}},{t \geq t_{0}}}.} & (9)\end{matrix}$

Normalized pressure and flow in (8) and (9) can be similarly defined byreplacing t* with t**, where t** is any time point between to and theend of the inspiratory phase of the breath cycle.

The present disclosure extracts the following features from the Deltawaveform to identify potential patterns associated with asynchrony. Thegrouping together of one or more features (e.g., a set of features)extracted from a Delta waveform forms a feature vector for that breathcycle.

Feature 1—Valley depth, defined as the difference between the value ofthe Delta waveform at a local minimum (valley) and a subsequent localmaximum (peak), is chosen as a feature. FIG. 6 depicts the Deltawaveform of a sample breath cycle involving premature termination thatcontains a single valley. There can be multiple valleys present in theDelta waveform, and for each valley the same definition of valley depthis used.

Feature 2—The second feature is defined as the maximum cross-correlationcoefficient between the time derivative of the Delta waveform and themeasured flow waveform. In an alternative embodiment, this feature canbe defined as the maximum cross-correlation coefficient between theDelta waveform and the volume waveform (delivered tidal volume waveform)that is either calculated or received from a data source.

Feature 3—The third feature is the estimated inspiratory time{circumflex over (T)}_(insp). In some embodiments, the estimatedinspiratory time is defined as the duration of time during which thenormalized pressure waveform is larger than a specific threshold ε₁(e.g., ε₁=0.25 or 0.5). Specifically, {circumflex over(T)}_(insp)=t₁−t₀, where:

t ₀=min{t:p (t)≥ε₁}  (10)

t ₁=max{t:p (t)≥ε₁}  (11)

In another embodiment, time {circumflex over (T)}_(insp) is defined asthe moment at which the exhalation valve of the ventilator is openedfollowing the inspiratory phase (inspiration phase) of ventilation.

Feature 4—The ratio of area under the curve of the Delta waveform toarea under the curve of a right triangle,

$\frac{A_{1}}{A_{2}},$

is the fourth feature, where:

$\begin{matrix}{{A_{1} = {\int_{t_{0}}^{t_{1}}{{\delta (t)}{dt}}}},} & (12) \\{{A_{2} = {\frac{1}{2}{\hat{T}}_{insp}}},} & (13)\end{matrix}$

and t₀ and t₁ are given by Error! Reference source not found. and Error!Reference source not found., respectively, to define the (mechanical)inspiration phase of the breath cycle. FIG. 7 depicts the Delta waveformof a sample breath cycle involving delayed termination. A₂ is shown asthe shaded triangular region in FIG. 7.

Feature 5—The ratio

$\frac{T_{D}}{{\hat{T}}_{insp}}$

is the fifth feature as shown in FIG. 7, where T_(D)=t₁−t₂, and

t ₂=min{t: δ(t)≥1}.  (14)

Feature 6—The ratio

$\frac{T_{negative}}{{\hat{T}}_{insp}}$

is the sixth feature, where:

T _(negative) =f _(t) ₀ ^(t) ¹ f(t)−ε₂)dt,  (15)

ε₂ is some threshold (e.g., ε₂=−2 L/min), and

$\begin{matrix}{{f(x)} = \left\{ \begin{matrix}{1,} & {{x > 0},} \\{0,} & {x \leq 0.}\end{matrix} \right.} & (16)\end{matrix}$

Feature 7—The location of valley(s) detected in Feature 1 is selected asFeature 7. In some embodiments, the absolute time from the start of thebreath in seconds is used to quantify the location of the valley. Inother embodiments, the location of the valley is quantified as afraction of the length into a breath at which the valley occurs. Inanother embodiment, the location of the valley is quantified as afraction of the length of inspiration. In other embodiments, thelocation of the valley is measured from the start of expiration and isexpressed as the fraction of a duration of an expiration.

Feature 8—The number of valleys detected in the Delta waveform.

Detecting Irregular Breaths

A breath cycle is classified as irregular if at least one of thefeatures above is missing and cannot be determined. In addition, if allfeatures can be calculated but the value of one or more features fallsoutside the range of typical values for such features (e.g., if thedifference between the observed value and the average value of a featurebased on a labeled data set is larger than a threshold value), then thecorresponding breath cycle is classified as an irregular breath.

In some embodiments, an irregular breath can be detected by defining adistance metric between breaths to measure the similarity betweenwaveforms of breaths (e.g., flow waveform or pressure waveform). In someembodiments, the similarity metric is the cross-correlation between twotime-series.

In order to assess if a breath is irregular, the distance between thebreath cycle under consideration and a collection of previously recordedbreaths that are not considered irregular is calculated (i.e., syntheticbreath cycles generated through simulation and/or previously recordedbreath cycles classified or labeled as involving an asynchrony event ornormal breaths with no evidence of asynchrony). If the distance of thebreath under consideration from each individual breath in the collectionis more than a defined threshold, then the breath is classified asirregular.

Detecting Inadequate Support During Inspiration

To detect inadequate support during inspiration, Feature 1, Feature 2,and Feature 7 are used. In some embodiments, Feature 1 and Feature 7 aremodified, and the detection of a local minimum (valley) of the Deltawaveform is limited to the inspiratory phase. In some embodiments,Feature 2 is modified, and the maximum cross-correlation coefficientbetween the time derivative of the Delta waveform and the measured flowwaveform in the inspiratory phase is calculated (or between the Deltawaveform and calculated volume waveform in the inspiratory phase). Insome embodiments, a rule-based algorithm is used; if the value of thefeatures are above or below a defined threshold, an “inadequate supportduring inspiration label” is added to the breath. In some embodiments, astatistical classifier (e.g., random forest, support vector machine, orneural network) is used to detect the presence of inadequate supportduring inspiration. Whether inadequate support during inspiration labelis assigned or not, the breath is moved to the next step and furtheranalyzed for other types of asynchronies.

Detecting Ineffective Triggering

To detect ineffective triggering, Feature 1, Feature 2, and Feature 7are used. In some embodiments, Feature 1 and Feature 7 are modified, andthe detection of a local minimum (valley) of the Delta waveform islimited to the expiratory phase. In some embodiments, Feature 2 ismodified, and the maximum cross-correlation coefficient between the timederivative of the Delta waveform and the measured flow waveform in theexpiratory phase is calculated (or between the Delta waveform andcalculated volume waveform in the inspiratory phase). In someembodiments, Feature 7 is defined as the location of the valley ismeasured from the start of expiration and expressed as a fraction of aduration of expiration.

In some embodiment, a rule-based algorithm is used, where if Feature 7exceeds inspiratory time by a threshold and Feature 1 is larger than athreshold an “ineffective triggering label” is added. In someembodiments, a statistical classifier (e.g., random forest, supportvector machine, or neural network) is used to detect ineffectivetriggering. Whether an ineffective triggering label is assigned or not,the breath is moved to the next step and further analyzed for othertypes of asynchronies.

Ineffective triggering is considered a label and not a class. Eachbreath cycle is assigned to one class, but can have multiple labels. Insome embodiments, a breath cycle can include ineffective triggering anda second type of asynchrony (e.g., delayed termination). Thus,ineffective triggering is considered a label.

Detection of Premature Termination and Delayed Termination

Random forests classification is used to classify breaths into one ofthe following classes:

-   -   (i) premature termination;    -   (ii) delayed termination; and    -   (iii) no cycling asynchrony.

In some embodiments, other classifiers generally referred to assupervised learning classifiers such as support vector machines,Bayesian networks, or neural networks are used to classify breaths intoone of the three classes described above. In random forests and otherclassification frameworks such as neural networks, a probability isassigned to each class label. The class label with the highestprobability, or the class label with a probability higher than somethreshold value, can be selected as the predicted class label. Theprobability associated with a class label can also be used as a measureof a classifier's confidence in assigning a breath to the specificclass.

Using random forest or other classification frameworks, the classifieris trained on a training set of breath cycles, where each breath cycleis represented by extracted features discussed in Section 10 (FeatureExtraction from the Delta Waveform) and ground truth class labels. Insome embodiments, class labels can be provided by a clinician or a panelof clinicians, where pressure and flow waveforms are used for labelingor pressure and flow and other signals such as esophageal pressure ordiaphragm activity are used for labeling. In some embodiments, syntheticpressure and flow waveforms can be generated based on differentasynchrony scenarios and ground truth labels are known based on thesynthetic scenario generated. In some embodiments, training data fortraining the machine learning classifier includes both labeled data frompreviously collected patient data and synthetic data. Once theclassifier is trained on the training set and the internal parameters ofthe classifier (e.g., random forests) are determined, the classifier canbe used to predict class labels for new breath cycles. If the breath isassigned to the premature termination or delayed termination class, thenclassification for this breath is complete. If the breath is classifiedas no cycling asynchrony, then the breath is moved to the next step andfurther analyzed for a final analysis.

Final Analysis

In the final stage, a breath cycle that has been classified as nocycling asynchrony and has no labels associated with inadequate support,air trapping or ineffective triggering is classified as “no asynchrony”.A breath cycle that has been classified as no cycling asynchrony and hasa label associated with inadequate support, air trapping and/orineffective triggering is classified as “some form of asynchronypresent” with an associated label (i.e., inadequate support, airtrapping or ineffective triggering or a combination of these labels).

Notification and Recommendation Modules

Information on patient-ventilator asynchrony can be used within aclinical decision support system framework to display any detectasynchrony events. The clinical decision support system can also providedefinitions for each asynchrony event and provide recommendations to theend-user to address detected asynchronies. In some embodiments, the enduser is a respiratory therapist. In some embodiments, a fully automatedmechanical ventilator can be used, and information on detectedasynchronies can be sent to a mechanical ventilator's control unit tochange ventilator settings or mechanical ventilation mode.

The recommendation module can be implemented as a rule-based expertsystem such that if the asynchrony index exceeds a certain threshold,the end-user is notified and depending on the type of asynchroniespresent one or more recommendations are provided to address theasynchrony. The asynchrony index is the fraction of breaths (includingtriggered and un-triggered attempted breaths) with one or more detectedasynchronies over a period of time. In some embodiments, the asynchronyindex is determined over a period time of about 15 seconds, 30 seconds,1 minute, about 5 minutes, about 10 minutes, about 20 minutes, about 30minutes, about 40 minutes, about 50 minutes, about 60 minutes, about 70minutes, about 80 minutes, about 90 minutes, about 100 minutes, about110 minutes, about 120 minutes, about 3 hours, about 6 hours, about 12hours, or about 24 hours.

In one embodiment of a system for detecting patient-ventilatorasynchrony, a graphical user interface (GUI) is used for displayingprompts corresponding to detected patient-ventilator asynchrony, asshown in FIG. 23. In some embodiments, the GUI can display educationalinformation on the detected asynchronies when a user taps on aninformation icon as shown in FIG. 23. In some embodiments, the GUI candisplay one or more recommendations for mitigating detected asynchronieswhen a user taps on an information icon as shown in FIG. 23.

Framework to Determine Parameters of Classification and LabelingAlgorithms Using Synthetic Data Generated Using Simulation of ArtificialVentilation

As discussed above, the classification and labeling of each breath cycleinvolves feeding the breath cycle to a series of classifiers andlabeling algorithms. Classifiers and labeling algorithms can be eitherrule-based or based on a supervised learning machine learning algorithm(also referred to as a statistical classifier). In the rule-basedalgorithm, the value of one or more features extracted from waveformsare compared to one or more threshold values. In statisticalclassifiers, the classifier is first trained on a training set toidentify the specific parameters of the classifier. Once the training iscompleted, the classifier uses the trained model to classify or labelnew breath cycles.

One drawback of determining optimal thresholds of a rule-based system orgenerating a large enough training set for a statistical classifier isthat a data set from one or more patients is required. Furthermore, eachbreath in the data set requires a “ground truth” label or classcorresponding to each asynchrony event. For example, to assign a labelof class to each breath cycle, a common practice involves recording ofesophageal pressure or electrical activity of the diaphragm to identifyasynchrony events.

Alternatively, one may present the set of waveforms to a series of humanexpert to analyze each breath, one-by-one, to provide “ground truth”labels. In this disclosure, we present a framework to create “synthetic”waveform data using the model discussed below in Section MathematicalModeling of the Respiratory System. Specifically, we can create a seriesof synthetic breaths under different conditions involving asynchrony orno asynchrony. Such data set can be used to determine the optimal valueof thresholds for a rule-based algorithm or can be used as a trainingset for a statistical classifier. Since synthetic data is generatedaccording to different known asynchrony scenarios, the ground truthlabels for synthetic breaths are known. This framework presents a way totrain rule-based and machine learning classifiers on waveform data andthe corresponding ground truth label for each breath bypassing the needfor manual labeling of breaths.

Mathematical Modeling of the Respiratory System

Dynamics of the respiratory system can be approximated by a first-orderdifferential equation for a single-compartment lung model given by:

$\begin{matrix}{{{p_{aw}(t)} = {{R_{rs}{\overset{.}{V}(t)}} + {\frac{1}{C_{rs}}{V(t)}} - {p_{mus}(t)} + p_{0}}},{t \geq t_{0}},} & (17)\end{matrix}$

where t₀ is the start of the breath cycle,

$\begin{matrix}{{\overset{.}{V}(t)} = {C_{rs}\frac{dp_{c}}{dt}}} & (18)\end{matrix}$

R_(rs) is respiratory system resistance, C_(rs) is respiratory systemcompliance, {dot over (V)}(t)=q(t) is flow, V(t) is respiratory systemvolume, p_(mus)(t) is the amplitude of (negative) pressure generated byrespiratory muscles, and p₀ is a constant. Pressure, flow, and volumerelationships are assumed to be linear. Other substitutions can be madeto the model without departing from the spirit of generating syntheticdata for use in training a machine learning algorithm or tuning aclassifier.

The respiratory model in Equation (17) can be represented by anequivalent electrical analogue consisting of a series arrangement of: a)a resistor element with resistance R_(rs) representing the resistance ofthe total respiratory system; and b) a capacitor element withcapacitance C_(rs) representing total compliance of the respiratorysystem. FIG. 5 illustrates an electrical analogue of the respiratorysystem. Constant p₀ of Equation (17) is the sum of Extrinsic PEEP andIntrinsic PEEP in the electrical analogue.

A time-varying source representing a mechanical ventilator for the inletboundary condition is included in the electrical analogue and labeled asVentilator to represent the presence of a mechanical ventilator.Depending on the mode of ventilation, an equivalent pressure (voltage)waveform or flow (current) waveform can be used to model the pressureand volume control ventilation modes, respectively. Constant sources arealso added to model extrinsic PEEP p_(e) and intrinsic PEEP p_(i).

In another embodiment, R_(rs) can be further modified to have differentvalues during inspiration R_(rs,insp), and during expiration,R_(rs,exp). The governing equations for the model can then be split intoinspiratory and expiratory phases:

$\begin{matrix}{{{p_{aw}(t)} = {{R_{{rs},{insp}}{\overset{.}{V}(t)}} + {\frac{1}{C_{rs}}{V(t)}} - {p_{mus}(t)} + p_{0}}},{t_{0} \leq t \leq t_{1}},} & (19) \\{{{p_{aw}(t)} = {{R_{{rs},\exp}{\overset{.}{V}(t)}} + {\frac{1}{C_{rs}}{V(t)}} - {p_{mus}(t)} + p_{0}}},{t_{1} < t \leq t_{breath}},} & (20)\end{matrix}$

where the interval t₀≤t≤t₁ represents the inspiratory phase, theinterval t₁<t≤t_(breath) represents the expiratory phase, and the breathcycle having a total duration of t_(breath)−t₀ seconds. In otherembodiments, the single-compartment model can be extended to have morecompartments by addition of resistive and capacitive elements to theelectrical analogue without departing from the spirit of generatingsynthetic data for use in training a machine learning algorithm ortuning a classifier.

Creation of Synthetic Breaths and Feature Vectors

In one embodiment of creating a plurality of synthetic breaths(ventilator pressure and flow waveforms), the respiratory system ismodeled using the single-compartment lung model discussed in SectionMathematical Modeling of the Respiratory System. In the case of modelingventilation in pressure control mode, the ventilator inlet boundarycondition can be a rectangular pressure waveform such as that shown inFIG. 12A, annotated as the inlet boundary condition. Alternatively, inthe case of modeling volume control mode, the ventilator inlet boundarycondition can be any typical pattern of flow used for ventilation suchas descending ramp flow pattern shown in FIG. 10B, annotated as theinlet boundary condition. Whatever the mode of ventilation beingmodeled, the inlet boundary condition defines when the ventilator cyclesfrom inspiration to expiration (i.e., cycling off time), such as thoseshown in FIG. 10B for volume control mode and FIG. 12A for pressurecontrol mode.

In the case of modeling spontaneous efforts during mechanicalventilation, the pressure generated by the respiratory muscles can beany pulsatile pressure waveform typical of respiratory muscle effort. Insome embodiments, an exponentially rising and decaying pulse as shown inthe top panel of FIG. 9A, annotated as the muscle waveform, is used. Aplurality of variations can be made to the inlet boundary conditions,such as to their shapes, timings, and durations, as well as to themuscle pressure profile, to produce a plurality of synthetic waveforms.The plurality of variations can further include variations inrespiratory system resistance and compliance.

The respiratory system model can be implemented in vitro (e.g., aphysical model employing elements such as actuators, valves, bellows,etc.) or in silico (e.g., a computational model simulated on a digitalcomputer). In one embodiment of an in vitro representation, anelectrical analogue of the respiratory system can be implementeddirectly with electrical circuit components (e.g., resistors,capacitors, voltage sources, current sources). In other embodiments, anin vitro representation can involve use of a breathing simulatorattached to a mechanical ventilator. An embodiment of an in silicorepresentation includes use of a digital computer to numerically solvethe differential equations described in Section Mathematical Modeling ofthe Respiratory System. In some embodiments of an in silicorepresentation, an electrical analogue model of the respiratory systemcan be solved with electronic circuit simulator design software. Thismay involve laying out each discrete electrical circuit element (e.g.,resistors, capacitors, voltage sources, current sources) in a circuitschematic editor and using its supplied tools to simulate the model tooutput waveforms that approximate ventilator pressure (voltagewaveforms) and flow waveforms (current waveforms).

Model parameters may be chosen based upon ranges in published literatureor empirically varied to reproduce the ranges of waveforms encounteredin clinical practice. Variations in parameters can also be used togenerate varying degrees of patient-ventilator asynchrony. Since thespecific clinical scenario for each in vitro or in silico simulation isknown, the ground truth label for asynchrony type for the resultingbreath cycle is known as well.

In an embodiment for modeling delayed termination, an inlet ventilatorwaveform can be prescribed such that its cycling off time (i.e., time atwhich inspiratory phase ends and expiratory phase begins) exceeds thetime at which the muscle pressure begins to relax from its peak value,as depicted in FIG. 10B. In an embodiment to simulate cases of prematuretermination, an inlet ventilator waveform can be prescribed such thatits cycling off time precedes the time at which the muscle pressurebegins to relax, as depicted in FIG. 9B. In other embodiments, the timeat which the muscle pressure profile rises above its baseline value canbe varied to further simulate respiratory muscle activation occurringany time within the breath cycle, for instance, as shown in FIG. 12Awhereby the muscle profile rises above baseline about 0.2 seconds intothe breath cycle. In an embodiment to simulate cases with neitherpremature termination nor delayed termination, the muscle waveform canbe nullified to simulate a passive patient without respiratory muscleactivity, as shown in FIG. 11A. In other embodiments to simulate casesof synchronized muscle and ventilator assist, the cycling off time ofthe ventilator, as defined by the prescribed inlet boundary condition,can be set to about the time that the muscle profile starts to decay(relax), as shown in FIG. 12A. Other variations to the peak values ofthe muscle profile as well as inlet boundary conditions can be madewithout departing form the spirit of generating synthetic waveformsdepicting various cycling asynchronies.

An extensive database can be populated with a plurality of syntheticwaveforms. In alternative embodiments, stochastic perturbations canfurther be superimposed upon the synthetic waveforms to simulate sensorand/or process noise.

For each individual breath cycle contained within the database ofsynthetic waveforms, features such as those discussed in Section FeatureExtraction from the Delta Waveform are extracted and deposited into adatabase containing synthetic feature vectors, one vector for eachsynthetic breath cycle. Since asynchrony type corresponding to eachsimulation scenario is known by design, ground truth labels forasynchrony types are known.

In another embodiment, additional synthetic data is generated by use ofgenerative statistical models based upon features previously extractedfrom synthetic waveforms. Probability distributions can be placed overthe extracted features that are conditioned upon the known ground truthlabels (i.e., asynchrony type). Upon sampling from the probabilitydistributions, additional synthetic feature vector can be generated andentries can be added to the database of synthetic feature vectors tofurther augment the amount of data available for training machinelearning algorithms or tuning classifiers. A machine learning algorithmcan then be trained on the database of synthetic feature vectors usingthe ground truth labels as the target classification in a similar manneras previously described when real labeled patient data is used fortraining.

One skilled in the art will recognize that the disclosed features may beused singularly, in any combination, or omitted based on therequirements and specifications of a given application or design. Whenan embodiment refers to “comprising” certain features, it is to beunderstood that the embodiments can alternatively “consist of” or“consist essentially of” any one or more of the features. Otherembodiments of the invention will be apparent to those skilled in theart from consideration of the specification and practice of theinvention. It is noted that where a range of values is provided in thisspecification, each value between the upper and lower limits of thatrange is also specifically disclosed. The upper and lower limits ofthese smaller ranges may independently be included or excluded in therange as well. The singular forms “a”, “an”, and “the” include pluralreferents unless the context clearly dictates otherwise. It is intendedthat the specification and examples be considered as exemplary in natureand that variations that do not depart from the essence of the inventionfall within the scope of the invention.

1. A system for detecting patient-ventilator interaction comprising: adetection module comprising computer-executable instructions in operablecommunication with one or more computer processor, the detection moduleconfigured for and in operable communication with a ventilator foracquiring mechanical ventilator airway pressure, flow, and/or volumewaveform data; and a patient-ventilator interaction indicator module inoperable communication with the detection module, the patient-ventilatorinteraction indicator module comprising computer-executable instructionsin operable communication with one or more computer processor for:generating a patient-ventilator interaction indicator waveformcomprising a Delta waveform from said ventilator waveform data, whereinthe Delta waveform represents the difference between normalizedpressure, after correcting for positive end expiratory pressure (PEEP),and normalized flow waveforms; extracting a set of features from saidpatient-ventilator interaction indicator waveform; and determining andindicating a type of patient-ventilator asynchrony present, or absencethereof, associated with one or more breath cycle, whichpatient-ventilator asynchrony or absence thereof is based on theextracted set of features.
 2. The system of claim 1, wherein thedetection module is configured for acquiring the ventilator waveformdata directly or indirectly from a mechanical ventilator, directly orindirectly from another monitor or system for producing the ventilatorwaveform data, or directly or indirectly from an archive of in vivo, exvivo, in vitro and/or in silico data.
 3. The system of claim 2, whereinone or more of the processors is embedded in the mechanical ventilator,or another monitor or system or a networked computer system.
 4. Thesystem of claim 1, wherein the patient-ventilator interaction modulefurther generates an asynchrony index based on the type ofpatient-ventilator asynchrony present or absence thereof.
 5. The systemof claim 1, wherein the Delta waveform is defined as:${{\delta (t)} = {\frac{{p_{aw}(t)} - p_{e}}{{p_{aw}\left( t^{*} \right)} - p_{e}} - \frac{q(t)}{q\left( t^{*} \right)}}},{t \geq t_{0}},$where t is time, t0 is the time at the start of the breath cycle, p_(e)is positive end expiratory pressure (PEEP), paw is airway pressure, q isair flow, and t* is time of maximum flow (q(t)≤q(t*), t≥t₀).
 6. Thesystem of claim 1, wherein the Delta waveform is defined as:${{\delta (t)} = {\frac{{p_{aw}(t)} - p_{e}}{{p_{aw}\left( t^{**} \right)} - p_{e}} - \frac{q(t)}{q\left( t^{**} \right)}}},{t \geq t_{0}},$where t is time, t0 is the time at the start of the breath cycle, p_(e)is positive end expiratory pressure (PEEP), paw is airway pressure, q isair flow, t** is a time between t0 and t*, and t* is time of maximumflow (q(t)≤q(t*), t≥t₀).
 7. The system of claim 1, wherein theextracting of the set of features comprises extracting one or more ofthe following features: depth of valleys of the Delta waveform; maximumvalue of the Delta waveform within inspiration phase; area under thecurve of the Delta waveform within inspiration phase; maximumcross-correlation of the Delta waveform with a delivered tidal volumewaveform; area under the portion of the Delta waveform occurring withinapproximately the first third of the Delta waveform duration; maximumvalue of the Delta waveform occurring within approximately the firstthird of the Delta waveform duration; and locations of valleys of theDelta waveform.
 8. The system of claim 1, wherein thecomputer-executable instructions for determining and indicating the typeof patient-ventilator asynchrony present, or absence thereof, comprise arule-based algorithm configured to classify one or more of the breathcycles into one or more categories of patient-ventilator asynchrony. 9.The system of claim 8, wherein the rule-based algorithm classifies oneor more of the breath cycles into one or more categories ofpatient-ventilator asynchrony by comparing at least one value from theset of features with a predetermined threshold.
 10. The system of claim1, wherein the computer-executable instructions for determining andindicating the type of patient-ventilator asynchrony present, or absencethereof, comprise a machine-learning classifier trained with patientdata and/or synthetic data and configured to classify one or more of thebreath cycles into one or more categories of patient-ventilatorasynchrony.
 11. The system of claim 10, wherein the machine-learningclassifier classifies one or more of the breath cycles into one or morecategories of patient-ventilator asynchrony by comparing at least onevalue from the set of features with a predetermined threshold.
 12. Thesystem of claim 10, wherein the synthetic data is derived from in vitro,in silico, or both in vitro and in silico representation of arespiratory system interacting with a mechanical ventilator.
 13. Thesystem of claim 1, further including a graphical user interfacecomprising: at least one window for displaying detection of one or morepatient-ventilator asynchrony; one or more elements within the at leastone window for communicating the detected patient-ventilator asynchrony.14. The system of claim 13, further including one or more element withinthe at least one window comprising one or more recommendations formitigating the detected patient-ventilator asynchrony.
 15. The system ofclaim 13, further including one or more element within the at least onewindow comprising educational information on the detectedpatient-ventilator asynchrony.
 16. The system of claim 13, furtherincluding one or more elements within the at least one window forcommunicating the asynchrony index.
 17. The system of claim 1, furtherincluding a transmission module to transmit the determined and indicatedtype of patient-ventilator asynchrony or absence thereof to a remoteserver and/or smartphone and/or tablet.
 18. The system of claim 17,wherein the patient-ventilator interaction module further generates anasynchrony index based on the type of patient-ventilator asynchronypresent or absence thereof and the transmission module transmits anotification to a user when the asynchrony index exceeds apre-determined threshold.
 19. The system of claim 1, wherein the typesof patient-ventilator asynchrony are chosen from one or more ofinadequate ventilator support, double triggering, ineffectivetriggering, premature termination, delayed termination, flow starvation,air trapping, buildup of fluid in lungs and/or a ventilator circuit,and/or no asynchrony.
 20. The system of claim 1, wherein thecomputer-executable instructions for determining and indicating the typeof patient-ventilator asynchrony present, or absence thereof, comprise acascade of classifiers configured to classify one or more of the breathcycles into one or more categories of patient-ventilator asynchrony.